BaoCaoKhoaLuan_v1.2_PhuongHTM.docx

7/30/2019 BaoCaoKhoaLuan_v1.2_PhuongHTM.docx

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GVHD: ThS. Nguyn Hu Thng SVTH: Chu Hong Nht & H ThMinh Phng

ABSTRACT

Our thesis topic is object tracking using Particle Filter, which we will do research how

to track an object using Partcle Filter, building demo applications.

Object tracking in computer vision has been done research for many years, but so

far it is still considered an open problem. However, currently there is a method of

object tracking that its effectiveness has been proven in many studies around the world,

it is recognized as a "State of the art" - that is the Particle filte. So, we have carried out

to do resrearch interesting subject based on the guidance of teachers and the materials

of the university, the seminar on this subject.In this thesis, we limit to introduce the theoretical basis of the particle filter, and

base on open source of the other research to improve of its experimental application in

the situation of tracking moving objects selecting from the first frame or a specific

object (face, pedestrian ...) and build the performace assessment to demonstrate the

effectiveness of the object tracking method.


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LI M U.

ti lun vn ca chng ti l theo vt chuyn ng dng Particle Filter, trong

chng ti s nghin cu cch thc theo di mt i tng dng Partcle Filter, xy dng

ng dng thc nghim.

Theo vt chuyn ng trong cng ngh cm quan my tnh (computer vision)

c nghin cu trong nhiu nm, nhng cho ti nay n vn c xem l mt vn

m. Tuy nhin, hin nay c mt phng php theo vt chuyn ng m tnh hiu qu

ca n c chng minh trong nhiu nghin cu trn th gii, n c cng nhn l

mt State of the art chnh l Particle filte. V vy, chng ti tin hnh nghin

cu ti th v ny da trn shng dn ca thy c v cc ti liu ca cc trng

i hc, cc hi nghchuyn v ti ny.

Trong kha lun ny, chng ti gii hn trong vic gii thiu c sl thuyt ca

particle filter, da trn c sm ngun pht trin ci tin mt s xy dng cc bng

nh gi kt qu trong qu trnh thc hin cc thc nghim chng minh tnh hiu

qu ca phng php theo vt chuyn ng ny.


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LI CM N.

u tin, chng em xin gi li cm n chn thnh n hai thy Th.S Nguyn

Hu Thng v C.H Cp Phn nh Thng gip v gii thiu chng em n vi

ti kha lun ny. Khng nhng th, trong qu trnh thc hin kha lun, hai thy

ch bo v hng dn tn tnh cho chng em nhng kin thc l thuyt chuyn ngnh

thng qua cc sch, bi bo, cc bui thuyt trnh, cng nh cch xy dng b cc,

cch vit mt kha lun tt nghipHai thy gip chng em rt nhiu, gip

chng em hon thnh tt kha lun tt nghip.

Chng em xin chn thnh bit n hai thy ni ring v tt c cc qu thy c

Khoa Cng ngh Phn mmTrng i hc Cng Ngh Thng Tini hc Quc

gia TPHCM gip chng em rt nhiu trong qu trnh hc tp.

TP H Ch Minh, ngy 30 thng 12 nm 2011

Sinh vin

Chu Hong Nht

H ThMinh Phng


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NHN XT

(Ging vin hng dn)


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NHN XT

(Ging vin phn bin)


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MC LCABSTRACT ..................................................................................................................i

LI MU. ............................................................................................................ ii

LI CM N. ........................................................................................................... iii

NHN XT .............. .......... ........ .......... ......... .......... ......... ......... .......... ........ .......... ..... iv

NHN XT .............. .......... ........ .......... ......... .......... ......... ......... .......... ........ .......... ...... v

DANH MC CC BNG, S , HNH .............................................................. viii

1. Gii thiu: .......................................................................................................... 9

2. Phng php thc hin: .................................................................................... 18

2.1. Gii thiu chung v bi ton theo vt chuyn ng: .......................................... 18

2.2.1. Reference model: .......................................................................................... 20

2.2.2. Hm thc thi s so snh (similarity measure): ............................................... 21

2.3. C ston hc: ................................................................................................. 21

2.3.1. c lng bayes: .......................................................................................... 21

2.3.1.1. nh ngha theo kha cnh ton hc: .......................................................... 22

2.3.1.2. nh ngha theo kha cnh trng thi h thng: ........................................... 23

2.3.2. Phng php Monte Carlo:............................................................................ 24

2.3.3. Particle filter: ................................................................................................ 26

2.3.3.1. Particle filter: ............................................................................................. 26

2.3.3.2. Tnh ton trng thi (measure): ....................Error! Bookmark not defined.

2.3.3.3. Cch chn mu: ...........................................Error! Bookmark not defined.

2.3.3.4. Cc vn trong thut ton chn mu: ....................................................... 32

2.3.3.5. Ti chn mu (resampling): ....................................................................... 33


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2.4. Cc ci tin bi ton traking bng thut ton particle filter: .......... ......... .......... .. 36

2.4.1. M t m ngun tham kho: .......................................................................... 45

2.4.2. Ci tin m ngun: ..........................................Error! Bookmark not defined.

2.4.3. Kt hp tracking v nhn dng: ..................................................................... 47

3. C sd liu thc nghim: .................................Error! Bookmark not defined.

3.1. YouTube action dataset: ................................................................................... 55

3.2. UCF Sports Action Dataset: .............................................................................. 58

3.3. Highway Traffic Clustering Database: .............................................................. 59

3.4. Face dataset: ..................................................................................................... 60

3.5. Walk dataset: .................................................................................................... 60

4. Chng trnh demo:............................................Error! Bookmark not defined.

4.1. Bng iu khin: ............................................................................................... 49

4.2. Mn hnh thc thi: ............................................................................................ 51

5. ng gi thc nghim: ...................................................................................... 61

5.1. Cng thc nh gi thc nghim: ..................................................................... 55

5.2. nh gi thc nghim ca chc nng theo di vt thc chn bi ngi dng:61

5.3. nh gi thc nghim ca chc nng theo di khun mt: ............................... 63

5.4. nh gi thc nghim ca chc nng theo dingi i b: .............................. 63

6. Kt lun: ........................................................................................................... 63

7. Tham kho: .........................................................Error! Bookmark not defined.


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DANH MC CC BNG, S , HNH

HNH

Hnh 1: S gii thch qu trnh t tn hiu thc t ti c lng thut ton. ........... 27Hnh 2: M hnh xc sut ca particle filter. ............................................................... 28Hnh 3: Tnh ton trng s.......................................................................................... 34Hnh 4:Hnh nh ca b d liu Youtube action ......................................................... 58Hnh 5:Hnh nh ca b d liu UCF Sports Action ................................................... 59Hnh 6: Hnh nh ca b d liu Hightway Traffic Clutering...................................... 60Hnh 7: Hnh nh ca b d liu khun mt. ............................................................... 60

BNG

Bng 1:Cc phng thc theo vt v cc nghin cu tiu biu. .................................. 11Bng 2: Cc phng thc nhn dng khun mt v cc nghin cu tiu biu. ............ 15Bng 3: Kt qunh gi s lc ............................................................................... 16


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1. GII THIU:Theo vt i tng thng qua tng khung hnh ca mt chui hnh nh l mt

chc nng ch yu trong cc ng dng th gic my tnh (computer vision

applications) bao gm cc ng dng trong lnh va an ninh nh h thng camera

theo di truyn thng, m nhn vai tr theo di v cnh bo, gip gim st vin

khng phi trc tip quan st 24/24: pht hin chuyn ng v cnh bo xm

phm, pht hin cc tnh hung bt thng da trn nhn dng cng nh u

, cp ngn hng, nguy c cht ui; ng dng ph bin hin nay l theo di

lu thng: cnh bo sm tnh trng n tc, ghi nhn cc trng hp phng nhanh

lng lch, chp v truy sut s xe vi phm x l ...; mt ng dng khc ang

c nghin cu pht trin l iu khin xe t hnh, h thng camera ghi nhn

hnh nh xung quanh khi xe di chuyn, bng cm quan my tnh, nh v ln

ng, pht hin cc vt cn v xe khc, nhn bit cc bng ch dn ... iu

khin xe; ngoi ra cn mt sng dng khc tng tc gia ngi v my thng

qua c ng.

Hnh 1Theo di khch b hnh(ngun: IEEE Computer Vision and Pattern Recognition, 2007).

Hnh 2 - H thng camera iu khin xe t hnh SCABOR(ngun: Technological University of Cluj Napoca).


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Hin nay cn nhiu vn v cc li trong cc trng hp phc tp ca bi

ton theo vt i tng. Do , nhiu k thut c pht trin gii quyt cc

vn ca bi ton.

Theo vt da trn hnh nh: phng php ny trch xut ra cc c tnh

chung v sau nhm chng li da trn thng tin ngoi cnh mc cao hn.

in hnh, Intille et al. (1997) xut mt blob-tracker theo di con ngi

trong thi gian thc. Background c loi tr ly c phn foreground. Cc

khu vc foreground sau c chia thnh cc m mu da trn mu sc. Cch

ny nhanh, nhng n c mt bt li ln v kt hp cc m mu khi cc i

tng tin li gn nhau.

Theo vt da trn ng vin (contour):.vi gi thit rng cc i tng

c xc nh bi cc ng bao quanh vi mt s thuc tnh xc nh. Xy

dng cc m hnh hnh dng (ng vin), m hnh ng vin ng hc v cc

thng s hnh nh khc trong qu trnh theo di. in hnh nghin cu Yezzi and

Soatto (2003), Jackson et al. (2004), v Rathi et al. (2005). Yezzi and Soatto

(2003) xut mt nh ngha cho s bin dng chuyn ng v hnh dng p

dng cho i tng bin dng hay di chuyn

Theo vt da trn Filtering: Filter v Particle Filter c nghin cu.

Kalman Filter gii quyt vi vic theo di hnh dng v v tr theo thi gian trong

cc h thng tuyn tnh nng ng. Mc khc, Particle Filter khng gii hn cho

cc h thng tuyn tnh. tng c bn ca Particle Filter l mt gn ng

sau bng cch s dng b lc Bayesian mt quy bng cch s dng mt tp

hp ca cc ht c trng lng c giao. in hnh nh theo di cc hnh dng

i tng v v tr theo thi gian c x l bi Kalman Filtertrong trng hp

ca cc h thng tuyn tnh (Rehg v Kanade, 1994). i vi Kalman Filter th

c th p dng khi h tuyn tnh v c xt nhiu Gauss. iu ny thc s gy ra

nhiu trngi trong vic gii quyt nhiu vn trong thc tv nh ni

trn, cc o thu c thng l cc i lng phi tuyn v c phn phi phi

Gauss, Anderson v Moore nm 1979 a ra thut ton lc Kalman m rng


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(Extended Kalman Filter - EKF). Thut ton ny l mt trong nhng thut ton

tt nht gii bi ton phi Gauss v phi tuyn lc by gi. Thut ton ny hot

ng da trn tng tuyn tnh ha (Linearization) cc quan st thu c bng

cch c lng cc i lng ny bng mt chui khai trin Taylor. Tuy nhin,

trong nhiu trng hp, chui c lng trong EKF m hnh ha rt km nhng

hm phi tuyn v phn phi xc sut cn quan tm. V kt qu l thut ton s

khng hi t. Julier v Uhlmann nm 1996 xut mt thut ton lc theo hng

xp x mt hm phn phi xc sut dng Gauss ch khng xp x mt hm phn

phi phi tuyn bt k. Thut ton ny c t tn l Unscented Kalman Filter

(UKF). Thut ton ny c chng minh l c kt qu tt hn EKF. Tuy

nhin, gii hn ca UKF l n khng th c p dng trong cc bi ton c

phn phi phi Gauss tng qut

c chp nhn rng ri l Particle Filter, xt v mt hiu sut Particle Filter

hiu qu hn Kalman Filter (Chang et al, 2005), v Particle Filter a ra mt

framework theo vt i tng m khng b gii hn trong trng hp tuyn tnh.

Phn loi c im chnhCc nghin cu lin

quan

Theo vt da trn hnh nh

Blob-tracker Intille et al. (1997

Skin color and elliptical Huang and Trivedi (2004)

Continuous density

MarkovRabiner (1989)

Multi-color model Bhandarkar and Luo

Level-set method or

geometric

partial differential

Gan et al. (2005)

Skin color filtering Chen and Tiddeman

2D human appearance Thome and Miguet

Theo vt da trn ng Snakes Kass et al. (1987)


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vin

Active contour

Blake and Isard (1998)

Isard (1998) MacCormick

(2000)

Level set technique

Sethian (1989)

Yezzi and Soatto (2003)

Jackson et al. (2004)

Rathi et al. (2005)

Theo vt

da trn

filter

Theo vt da

trn Kalman-

filtering

Kalman filter (KF) Rehg and Kanade (1994)

Extended Kalman Filter Jebara et al. (1998)

KF with ellipse and colorZhao et al. (2004)

Girondel et al. (2004)

KF with elastic matching Luo and Bhandarkar

Theo vt da

trn Partilce

Filter

Condenstaion algorithm Isard (1998)

PF with partitioned MacCormick and Isard

PF with optimal proposal

distribution (OPD)Doucet et al. (2001)

Kalman particle filter

(KPF) and unscented Li et al. (2003)

PF with Markov random

fieldWang and Cheong (2005)

Kernel particle filter Chang et al. (2005)

PF with geometric active

contoursRathi et al. (2005)

Multiple-blob tracker

(BraMBLe)

Isard and MacCormick

(2001)

Boosted particle filter Okuma et al. (2004)

Bng 1:Cc phng thc theo vt v cc nghin cu tiu biu.


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Ngoi ra, trong kha lun ny s c s dng bi ton nhn dng i tng c

th(in hnh quan trng l khun mt); do chng ta s gii thiu v bi ton

nhn dng. Cng tng bi ton theo vt i tng th pht hin v nhn dng

mt i tng c th cng c ngh quan trng trong ng dng th gic my

tnh. Mt ng dng gn gi nht l nhn dng khun mt ph bin trn mt s

dng my tnh xch tay, hin ti c k thut nhn dng khun mt kh hon

thin: pht hin khun mt bng cch s dng phng php my hc v d ton

thng k chng minh kt qu xut sc trong tt ccc phng php nhn din

khun mt hin c. Nhiu nghin cu c tin hnh trong khu vc k thut

nhn din khun mt, chng hn nh AdaBoost (Viola v Jones, 2001a; Viola v

Jones, 2001b), FloatBoost (Li et al, 2002.), S-AdaBoost (Jiang v Loe, nm

2003), mng n-ron (Rowley et al, 1996; Curran et al, 2005), Support Vector

Machines (SVM) (Osuna et al, 1997, Shih v Liu, nm 2004), m hnh Markov

n (Rabiner v Jung, nm 1993), v phn loi Bayes (Schneiderman v Kanade

nm 1998; Schneiderman, 2004). Viola v Jones (2001a, 2001b) xut mt

thut ton nhn dng khun mt AdaBoost, c th pht hin khun mt trong

mt cch nhanh chng v mnh m vi t l pht hin cao. Li et al. (2002)

xut cc thut ton FloatBoost, mt phin bn ci tin ca AdaBoost, ci thin

khnng hc cc lp phn loi tng nhm gim t l li. Jiang v Loe (2003)

xut S-AdaBoost, mt bin th ca AdaBoost, x l trong vic pht hin m hnh

v phn loi. Rowley et al.(1996) thc hin cc nghin cu quan trng nht

trong s tt ccc phng php nhn din khun mt da trn cc mng n-ron.

H s dng mt mng li n-ron a lp hc m hnh khun mt v non-face

t cc b hnh nh v khun mt v non-face. Mt nhc im ca phng php

ca h l ch phi i mt thng ngpha trc c thc pht hin. Mc d

Rowley et .al. ci tinphng thc c th pht hin hnh nh khun mt xoay,

tuy nhin kt qu khng tt v t l nhn dng kh thp. Support Vector Machines

(SVMs) s dng cu trc gim thiu ri ro gim thiu trn rng buc ca cc

li d kin tng qut (Osuna et al, 1997, Shih v Liu, nm 2004). Nhng kh


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khn chnh ca SVMs l tnh ton nhiu v yu cu b nhcao. M hnh Markov

n (HMMs) cho rng dng khun mt v non-face c thc m tnh l tham

s ngu nhin (Rabiner v Jung, 1993). Mc ch ca hun luyn HMM l

c tnh cc thng s thch hp trong m hnh HMM ti a ha kh nng

quan st d liu c hun luyn. Schneiderman v Kanade (1998) trnh by mt

lp phn loi Bayes thun, trong d tnh xc sut xut hin v v tr ca mt

m hnh khun mt quy m nhiu. Tuy nhin, vic thc hin phn loi Bayes

thun l thp. gii quyt vn ny, Schneiderman (2004) xut mt mng

Bayesian hn ch pht hin i tng.Phng php ny tm kim cc cu trc

ca mt phn loi da trn mng Bayes trong khng gian rng ln ca cu trc

mng c th xy ra

Phn loi c im Nghin cu tiu biu

Phng thc da trncc c trng

Facial features with edgesand lines

Herpers et al.(1996) Song et al.2002

Gray scale Yang and Huang(1994) Grafet al.1995

Skin color and elliptical edges Huang and Trivedi (2004)McKenna et al. (1998)

Naseem and Deriche(2005)

Multiple facial features Huang et al. (2004)Wang and ertMariani (2000)

Phng thc da trnmu

Elastic bunch graph matching Wiskott et al. (1997)

Snakes and templates Kwon and Lobo (1994)Gunn and Nixon (1996)

Silhouettes Samal and Iyengar (1995)

Phng thc

datrnhnhnh

Hc my

AdaBoost Viola and Jones (2001a;2001b)Lienhart and Maydt(2002) Wang et al.

FloatBoost Li et al. (2002a; 2002b)

S-AdaBoost Jiang and Loe (2003)


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AdaBoost and PCA Zhang et al. (2004b)

AdaBoost with look-up-tabletype weak classifiers

Wu et al. (2004)

AdaBoost with Gabor features Yang et al. (2004)

Mng n-ron

Multilayer neural networks Rowley et al. (1996;1998) Curran et al.2005

NN and ConstrainedGenerativeModel

Fraud et al. (2001)

Support

VectorMachines

(SVM)

SVM with polynomial kernel Osuna et al. (1997)

SVM with Orthogonal Fourierand Mellin Moments (OFMM) Terrillonet al. (2000)

SVM with DiscriminatingFeature Analysis

Shih and Liu (2004)

Cc thut tonkhc

Hidden Markov Model(HMM)

Rabiner and Jung (1993)

Naive Bayes classifier Schneiderman and Kanade(1998)

Restricted Bayesian network Schneiderman (2004)

Face Probability GradientAscent (FPGA)

Parket al. (2005)

Bng 2: Cc phng thc nhn dng khun mt v cc nghin cu tiubiu.

Trong kha lun ny, chng ti s tip cn bi ton theo vt chuyn ng

theo hng kt hp vic nhn dng i tng v theo di i tng c th l hai

i tng khun mt (face) v ngi i b (pedestrian) tin hnh nhn dng i

tng (dectec) sau theo vt (track) v sau mt khong thi gian s tin hnh

nhn dng i tng nhm lm tng hiu qu theo vt; ci tin s lng i

tng theo vt c th l cng mt thut ton nhng p dng cho cc nhm mu

(particle filter) khc nhau-mi nhm tng ng vi mt i tng c theo vt;

ngoi ra ci thin tc x l ca thut ton bng k thut lp trnh song song v


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s dng thread tng hiu qu ca thut ton. Bn cnh , chng ti tin hnh

nh gi hiu qu ca cc ci tin bng cc d liu ly t bi bo nghin cu c

ting (nhm m bo tnh hp lv tnh ng n ca d liu), cc b d liu c

cc c trng v phc tp phong ph: d liu YouTube action bao gm cc

video c cng chung mt sim nh ngoi cnh, cng view quan st,

phc tp ca c sd liu: sthay i ln trong chuyn ng camera, s xut

hin i tng v t ra, quy m i tng, quan im, nn ln xn, iu kin

chiu sng, v.v; b d liu UCF Sports Action liu gm cc video phn

gii 720x480, m t cc hot ng th thoa c trng trong mt lot cc ngoi

cnh khc nhau

# Tn video S frame Kt qu (%)

i

tng

bt k

v_jumping_01_04 201 94.03

v_jumping_02_03 201 48.76

v_jumping_02_04 201 51.74

v_jumping_03_01 201 84.08

Khun

mt

jam1 199 83.42

jim2 199 95.48ssm1 199 100

Ngi

i b

v_walk_dog_01_04 151 74.83

walk002 100 56

walk008 101 11.88

walk014 100 100

Bng 3: Kt qunh gi s lc

Trong cc phn sau ca bi lun vn c t chc nh sau: mc ba gii

thiu chung v cch tip cn chung ca bi ton theo vt i tng, l thuyt v

particle filter v cc im ci tin, mc bn m tcc database c s dng

trong thc nghim nh gi hiu qu ca thut ton v mc nm a ra kt qu
http://server.cs.ucf.edu/~vision/projects/action_mach/ucf_sports_actions.ziphttp://server.cs.ucf.edu/~vision/projects/action_mach/ucf_sports_actions.zip


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ng gi thc nghim da trn cc cng thc c chng minh trong cc hi

ngh khoa hc.


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2. BI TON THEO VT I TNG:2.1.Tng quan v bi ton theo vt i tng:Mc tiu ca bi ton theo vt i tng l xc nh i tng thnh phn cng

vi v tr v ng tc chuyn ng tng ng ca chng nhm a ra nhng

quyt nh iu khin thch hp.

Hu ht kh khn ca bi ton theo vt i tng l do kh nng bin

ng ca nh video biv cc i tng theo vt thng l cc i tng video.

Khi mt i tng chuyn ng qua mt vng quan st trn khung hnh, hnh

nh vi tng c ththay i rt nhiu. Sthay i ny n t 3 ngun chnh:

sthay i t thi tng ch (nh ngi ang ng chuyn sang t th ngi;

xe ang i thng quo sang tri ) hay s bin dng ca i tng ch, s thay

i v sng, v s che khut mt phn hay ton bi tng ch (nh khi

hai ngi hay xe i ngang qua nhau).

Mi phng php tip cn c cc u nhc im ring nhng tng qut

c thchia ra thnh hai hng ch yu:

Hng top-bottom: xut pht t cc quan st, thc hin rt trch, phnon cc hnh nh hay cc khung hnh u vo tm ra i tng cn

theo vt. V d, phng php theo vt dng Blod detection


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quan st thu c. C thhn, u tin, pht sinh ra mt tp cc gi

thuyt c th c trong khng gian trng thi ca h thng, sau s

dng quan st tnh likelihood cho tng gi thuyt, cc likelihood

ny s quyt nh n mc tin cy ca tng gi thuyt (thng

c biu th bng cc trng s). Cui cng tng hp tp cc gi

thuyt-trng scho c lng trng thi ca h thng.

2.2.Cch tip cn chung ca bi ton theo vt i tng:i tng theo vt c thchia thnh ba nhm i tng chnh:

Nhm cc i tng ring bit c ccmt tpc tnhphn bitchungnh xe hi, ngi, khun mt.

Nhm cc i tng ring bit kt hp vi mt thuc tnh c thnhxe t chy, ngi i b.

Nhm cc i tng khc bit nhng c chung mt thuc tnh c thnh cc i tng di chuyn.

Thut ton theo vt i tng thc cht lm tm mt vng nh di chuyn t

frame khung hnh ny sang khung hnh frame khc nh th no nn mi nhm

i tng sc cc c im ring nhng tng qut ta c cc bc chnh nhsau:

Th nht, ta cn xy dng mt reference model m t cho itng cn theo vt.

Sau trn mi input framekhung hnh u vo (input frame), datrn cc hm thc thi so snh (similarity measure) thut ton tm

(localize) vng no m gn ging vi m hnh tham chiu - reference

model nht.

2.2.1. M hnh tham chiu (Reference model):M hnh tham chiu (reference model) l m hnh m t cc thng tin vv b

ngoi ca i tng cn theo vt. xy dng m hnh tham chiu cho i

tng, cch thng dng nht trong cc ng dng theo vt i tng l dng m


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hnh mu (color-model) bn cnh cc c trng vng bin (contour), chuyn

ng tuy nhin c mt s vn t ra:

H mu no c dng RGB hay HSV, ... Lu rng khi chng tadng m hnh mu m hnh tham chiu c ngha l chng ta chu

thm mt gi s l chng ta ch theo vt c cc i tng trn nh

mu ch ko phi l nh bt k. Ngoi ra, cng cn chn k h mu v

n rt nhy cm vi sng, khung cnh. Hin ti trong ng dng th

nghim ang s dng vi h mu HSV

M hnh phn b (distribution) nh th no C nhiu cch mhnh phn b (distribution) nh Gaussian, hoc Mixture Gaussian,

hoc ch n gin nh histogram. Trong ng dng th nghim ang s

dng histogram.

2.2.2. Hm thc thi s so snh (similarity measure): so snh gia i tng m hnhtham chiuch (target object)hay i

tng cn theo vt v m hnh tham chiu (candidate object reference model)

trongca mi khung hnh u vo (input frame), chng ta phi cn phi c mt

hm tnh ton m t s gn nhauging nhau (similarity measure). Hm ny c

nhim v s tnh ton mc tng ng/ging nhau gia hai i tng trn t

xc nh c trng thi ca i tng cn theo vt. V d, hm SSD (Sum of

Squared Differences) c dng trong trng hp tha iu kin sng khng

i ngha l gi trnh sng ca cc im nh khng thay i t khung hnh ny

sang khung hnh khc; hm SAD (Sum of Absolute Differences).

2.3.C ston hc:2.3.1. M hnh Markow n (Hidden Markow models-HMM):

M hnh Markow n l m hnh thng k trong h thng c m hnh ha

c cho l mt qu trnh Markov vi cc tham s khng bit trc v nhim v

l xc nh cc tham sn t cc tham s quan st c, da trn s tha nhn

Comment [c1]: Ti sao


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vi l vect nhiu (ngu nhin), xc sut chuyn trng thi tnhc t m hnh ny.

M hnh quan st: m t mi quan h gia trng thi quan st v trng

thi cng thi im:

eq. 3vi l vect nhiu (ngu nhin), m hnh ny c s dng tnh

likelihood | .Ti mt thi im k bt k, hm phn phi xc sut hu nghim c cho

bi quy tc Bayes nh sau

| eq. 4Gii php Bayes cho rng chng ta c tht c mt xc sut hu

nghim (posterior density) | qua hai bc:Don:

| || eq. 5Cp nht:

| || || eq. 6Tuy nhin c lng ny ch mang tnh l thuyt v khng c phng

php tng qut tnh tch phn trong cng thc (eq.4) v (eq.5) trong trng

hp lin tc v nhiu chiu. V l do , cc phng php lc phi tuyn nh lc

Kalman, lc Kalman mrng, lc tng hp Gauss, ra i nhm mc ch xp

x hm mt hu nghim. Nhng nu cc phng php lc Kalman, lc

Kalman mrng, lc tng hp Gauss, da vo gii tch, tm kim li gii cho

cc phng trnh (eq.4) v (eq.5) bng mt hay nhiu phng trnh khc vi gi

s rng mi trng tha mn mt s yu cu, cn phng php Monte Carlo li

da vo s m phng v xp x cc hm phn phi v cc tch phn bng mt tp

cc d liu c sinh ra bng chnh hm phn phi.

2.3.3. Phng php Monte Carlo:


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Trong phn ny, chng ta s xem xt mt trong nhng nn tng l thuyt quan

trng nhtphng php Monte Carlo ca lc Particle. Khng mt tnh tng

qut, ta xem xt bi ton tnh tch phn trong d liu rt ln, nhiu chiu

(High-Dimensional Intergral) nh sau

| eq. 7Trong , l mt hm |- kh tch. Gi s ta c th sinh ngu

nhin N mu ngu nhin phn phi c lp v ng nht * + tphn phi xc sut |. Nh vy, phn phi xc sut |c thc clng nh sau

eq. 8

Trong , k hiu hm delta-Dirac c tm ti . Vy, cthc xp x bng tch phn Monte Carlo (Monte Carlo Integration) nh sau

eq. 9

Biu thc ng lng trong (eq.8) hp l v theo lut mnh s ln, nuphng sai ca tha |,- th phng saica c cho bi ()

Vy ta c

eq. 10

Trong l k hiu ca hi t hu chc chn (Almost Sure

Convergence). Hn na, v (hu hn) nn nh l gii hn trung tm

c tha, ngha l , - ( ) eq. 11Trong k hiu cho hi t trong phn phi xc sut. T nhng lp

lun trn, suy ra dng tp cc mu ngu nhin * + c th d dng


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c lng c . Da vo c lng ny, kt hp vi phng trnh (eq.10),ta cng c th ddng tnh c mc hi t ca php c lng, hay mc

li ca n.

Khng nhng th, im mnh ca phng php tch phn Monte Carlo

cn nm ch n khng ph thuc vo s chiu ca d liu. Tht vy, nu ta

phi tnh (eq.6) bng phng php xp x tch phn Riemann, trong khng

gian trng thi c m hnh ha bng mt phng trnh gii tch, chnh xc

ca php xp x sl i vi tch phn trn min d liu c s chiu l , ngha l

mc hi t ca php xp x s l

i vi tch phn trn min d liu

c s chiu l , ngha l mc hi t ca php xp x cng gim khi s chiuca php tnh tch phn cng tng. Trong khi , p dng phng php tch phn

Monte Carlo, phng php m phng ngu nhin khng gian trng thi t phn

phi xc sut ca n, chnh xc ca php xp x l v khng phthuc vo s chiu ca d liu. iu ny c ngha l, phng php tch phnMonte Carlo c lp vi s chiu ca php tnh tch phn.

Tuy nhin, mt vn gp phi khi p dng phng php tch phn

Monte Carlo chnh l lm sao c th to ra mt tp cc mu ngu nhin tphn phi xc sut ch | bt k mt cch hiu qu. Tuy nhin thngkhng c cch no sinh ra tp mu ny mt cch trc tip t phn phi xc

sut ch | v | trong trng hp tng qut, thng l a bin vkhng c mt dng chun nht nh m chng ta c th bit trc (dng ca

| c th bin i theo thi gian). Do , ta phi dng phng php gintip sinh ra tp cc mu d liu ny. Vn ny sc cp chi tit trong

phn

2.4.Particle filter:2.4.1. nh ngha:

Theo vt i tng bng phng php particle filter l phng php da trn xc

sut, s dng cc phng trnh don (prediction) don trng thi ca

Comment [c2]: Khng nn trnh by theo ccny


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i tng v phng trnh cp nht (updation) hiu chnh li cc d on

trc v trng thi ca i tng da trn nhng tri thc thu thp c t cc

quan st (observation) trn i tng.

Hnh 3: S gii thch qu trnh ttn hiu thc t ti c lng thutton.

Phng php theo vt i tng dng particle filter s dng m hnh ng,

cng vi cc quan st trc quan, thit lp cc ngu nhin theo thi gian; p dng

m hnh xc sut da hnh dng v chuyn ng ca i tng phn tch cc

dng d liu t video.

Phng php ny dng c lng Bayes hi quy lm gii php l thuyt, v

tng ca phng php Monte Carlo xp x cho gii php l thuyt ny.

Cng nh cc phng php lc phi tuyn khc, lc particle cng tnh xp x hm

mt hu nghim tuy nhin khng nh cc phng php khc da vo gii

tch, c gng tm mt li gii cho cc phng trnh trn thng qua mt hay nhiu

phng trnh khc, th lc particle li s dng mt tp ln cc mu d liu c

pht sinh t bi chnh cc hm phn phi trong cc tch phn ny.


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Hnh 4: M hnh xc sut ca particle filter.

2.4.2. C sl thuyt:K thut theo vt i tng ny da trn cc trng thi trc ca chuyn ng

don v tr trong khung hnh tip theo. phn tch k thut s, qu trnh

tuyn truyn c xc nh ti thi gian ri rc.Cc thut ng sau c s dng

trong thut ton particle filter:

xt: trng thi ca i tng vo thi im t.

zt: cc tn hiu quan st t d liu trong khung hnh ti thi im t.

Tp hp cc trng thi ca i tng ttrc ti thi im t (x1, x2, xt).

Tp hp cc d liu quan st c ti thi im t (z1, z2, ... zt).

V mt l thuyt, mt quan st c trng ca bin i thng k ca z cho

x, c thc c tnh cho zt cho xt ti bt k thi im t. Trng thi tip theo l

c tnh theo trng thi, cc o lng hin ti v m hnh chuyn ng tham

chiu.

Gi thit rng cc framework v xc sut ca m hnh ng l da trn chui

Markov - l mt chui cc thnh phn c to ra t cc thnh phn trc v cc

thnh phn tng lai ch ph thuc vo trng thi hin. Do trng thi ti thi

im t c ginh l ch ph thuc vo trng thi ti thi im t-1, c lp vi

tp hp cc trng thi trc thi im t-1:


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| | eq. 12

S dngphng trnh th hai khc bit gia ngu nhin trong thi gian

ri rc, hon ton xc nh bi mt phn biu kin |2.4.3. M hnh quan st (Observation model):

M hnh quan st chnh l c scho nhng php o v tnh ton xc sut trong

cc phng trnh xc sut ca h.

Gi thit rng d liu quan st ztc lp vo cc d liu quan st ti thi

im t-1 v mi thi im trc t-1.

Hnh 5: Trng thi x v cc dliu quan st z ch ph thuc vo trng thihin.

Mc ch ca bc quan st o lng kh nng mi particle c d

on khp nh th no so vi cc d liu quan st. M hnh quan st s dng

cc c tnh ca hnh nh, chng hn nh cnh, mu sc, biu (histogram), vv,

xc nh trng thi don da trn mu cc d liu u vo hoc d liu

quan st.

Qu trnh quan st c xc nh bi mt hm mt quan st |,trong xc nh xc sut hu nghim ca cc quan st tnh ton zt cho mt

trng thi nht nh xt. Da vo cng thc | | th | c ths dng trong sut theo vt i tng (Blake v Isard, 1998).

2.4.4. M hnh ng:M hnh ng ca i tng chnh l nhng phng trnh xc sut m t chuyn

ng, bin i ca cc i tng trong h.

Gi nh l m hnh xc sut ca m hnh ng c da trn mt chui

Markov- l mt lot cc yu t tng c to ra t cc yu ttrc v trng thi

x1 x2 ........... xt-1 xt

z1 , z2 ............ zt-1, zt


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tng lai ch ph thuc vo trng thi hin ti bt k trng thi trc nh th

no.Sau , nh nc c ginh l ch ph thuc vo trng thi trc , c

lp ca lch strc ca n.:

2.4.5. Cc bc thc hin:Cc bc trong qu trnh xc nh trng thi ca i tng thng qua

particle filter

Khi to trng thi i tng x0t khung hnh u tin

To ra mt tp hp mu gm N phn t (particle) {xtm}m=1...N

D on cho miparticlebng cch s dng second order auto-regressive dynamics

Tnh ton trng s cho mi particle trong tp hp mu bngtnh khong cch gia

Ti chnmt tp hp ccparticle da vo trng sca tphp cc particle to ramt tp hp mi ca ccparticle

c trng s

Xc nh trng thi i tng ti thi im hin ti da voparticle c trng s ln nht


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Bao gm ba bc chnh nh sau:

D on (predict) xc sut ca trng thi i tng ti thi im t datrn thng tin trng thi ti thi im t-1.

Tnh ton trng thi i tng (measure) da trn cc quan st (tn hiu tvideoso snh cc histogram ca cc mu) ti thi im t hin ti, t

suy ra xc sut ca mu ging vi i tng.

Ti chn mu (resample) hay chnh xc hn l cp nht trng s w cacc mu

Formatted: Font: (Default) Times NewRoman, 13 pt, Not Bold, Font color: Auto


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Hnh 6: Tnh ton trng s

Trong nhng phn trn, ta bit

| eq. 17chnh l mt c lng ri rc ca hm mt |. Sau mt vi ln thchin, tt c cc mu u c trng s rt nh, ngoi tr mt phn t duy nht

trong tp hp mu c trng s bng 1. Tuy nhin, ta nhn thy khng phi tt c

cc mu u thc s gp phn quyt nh vo gi tr ca hm mt hu nghim

| m ch c nhng phn t trong tp hp mu tng i gn vi kvng mi c ng gp ng k trong vic quyt nh gi tr ca hm.Phng php ti chn mu gii quyt vn ny bng cch sp xp v iuchnh li N phn t trong mu c sn xp x tt hn hm mt ny.

Gi l vector trng thi trc khi ti chn mu v l vector trngthi sau khi bc ti chn mu c thc hin. Vy th tc ti chn mu chnh l

nh x

{ }

eq. 18

Sao cho

( ) Bng cch sp xp li N phn t trong mu v t li cc trng s mi,

thut ton trnh c hin tng thoi ha (ti mi thi im, trng s ca cc

phn t trong mu u nh nhau v bng 1/N) v gip cho thut ton SIS tp


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trung vo nhng vtr ha hn nht trong khng gian trng thi ti c i

tng cn quan tm.


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3. PHT HIN I TNG (DECTECTOR)Trong chng ny, chng ta s tm hiu vphng php pht hin i tng

c p dng trong ng dng thc nghim trn hai i tng l khun mt v

ngi i b.

3.1.Bi ton pht hin i tng:Hin nay c rt nhiu phng php pht hin i tng, da vo cc tnh cht

ca cc phng php, ta c th chia ra lm bn hng tip cn chnh nh sau:

Hng tip cn da trn tri thc: m ho hiu bit ca con ngi v ccloi i tng v to ra cc tp lutxc nh i tng.

Hng tip cn da trn cc c tkhng thay i: mc tiu cc thutton tm ra cc c trng m t cu trc i tng(cc c trng khng

thay i so vi t th, vtr t thit b thu hnh hay khi sng ti thay

i ...).

Hng tip cn da trn so khp mu: dng cc mu chunhay cc ttrng ca khun mt ngi.

Hng tip cn da trn din mo: phng php hc tmt tp nh hunluyn muxc nh khun mt ngi.

ng dng th nghim trong kha lun ny s dng hng tip cn da trn

din mo, s dng b phn loi mnh (strong classifier) AdaBoost l s kt hp

ca cc b phn loi yu (weak classifier) da trn cc t trng Haar-like xc

nh i tng. M ngun ci t trong ng dng thc nghim da trn th vin

m ngun mOpenCV ca Intel.

3.2.c trng Haar-like:Do Viola v Jones cng b, gm 4 c trng c bn xc nh i tng. Mi

c trng Haarlike l s kt hp ca hai hay ba hnh ch nht "trng" hay "en"nh trong hnh sau:


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Hnh 7: c trng Harr-like c bn.

s dng cc t trng ny vo vic xc nh khun mt ngi, 4 t

trng Haar-like c bn c mrng ra, v c chia lm 3 tp c trng nh

sau:

1. c trng cnh (edge features):

Hnh 8: c trng cnh.

2. c trng ng (line features):

Hnh 9: Cc c trng ng.

3. c trng xung quanh tm (center-surround features):

Hnh 10: Cc c trng xung quanh tm.

Dng cc c trng trn, ta c thtnh c gi tr ca c trng Haar-like l

s chnh lch gia tng ca cc im nh (pixel) ca cc vng en v cc vng

trng nh trong cng thc sau:

eq. 19S dng gi tr ny, so snh vi cc gi tr ca cc gi trim nh th, cc

c trng Haar-like c thtnggim sthay i in-class/out-of-class (bn trong

hay bn ngoi lp i tng), do s lm cho b phn loi dhn.
http://2.bp.blogspot.com/-l-KNt0P3waU/TzI5EbCYq9I/AAAAAAAAAe4/vMFZjftJHpY/s1600/Haarlike-2.pnghttp://2.bp.blogspot.com/-l-KNt0P3waU/TzI5EbCYq9I/AAAAAAAAAe4/vMFZjftJHpY/s1600/Haarlike-2.pnghttp://4.bp.blogspot.com/-J3I13L81GDw/TzI5PBONVRI/AAAAAAAAAfA/lcj_rZ2k8e4/s1600/Haarlike-3.pnghttp://4.bp.blogspot.com/-J3I13L81GDw/TzI5PBONVRI/AAAAAAAAAfA/lcj_rZ2k8e4/s1600/Haarlike-3.pnghttp://4.bp.blogspot.com/-J3I13L81GDw/TzI5PBONVRI/AAAAAAAAAfA/lcj_rZ2k8e4/s1600/Haarlike-3.pnghttp://4.bp.blogspot.com/-J3I13L81GDw/TzI5PBONVRI/AAAAAAAAAfA/lcj_rZ2k8e4/s1600/Haarlike-3.pnghttp://2.bp.blogspot.com/-l-KNt0P3waU/TzI5EbCYq9I/AAAAAAAAAe4/vMFZjftJHpY/s1600/Haarlike-2.pnghttp://1.bp.blogspot.com/-VfVkuegsiwU/TzI414grUcI/AAAAAAAAAew/yWbLYpIyYeQ/s1600/image002.pnghttp://4.bp.blogspot.com/-J3I13L81GDw/TzI5PBONVRI/AAAAAAAAAfA/lcj_rZ2k8e4/s1600/Haarlike-3.pnghttp://2.bp.blogspot.com/-l-KNt0P3waU/TzI5EbCYq9I/AAAAAAAAAe4/vMFZjftJHpY/s1600/Haarlike-2.pnghttp://1.bp.blogspot.com/-VfVkuegsiwU/TzI414grUcI/AAAAAAAAAew/yWbLYpIyYeQ/s1600/image002.pnghttp://4.bp.blogspot.com/-J3I13L81GDw/TzI5PBONVRI/AAAAAAAAAfA/lcj_rZ2k8e4/s1600/Haarlike-3.pnghttp://2.bp.blogspot.com/-l-KNt0P3waU/TzI5EbCYq9I/AAAAAAAAAe4/vMFZjftJHpY/s1600/Haarlike-2.pnghttp://1.bp.blogspot.com/-VfVkuegsiwU/TzI414grUcI/AAAAAAAAAew/yWbLYpIyYeQ/s1600/image002.pnghttp://4.bp.blogspot.com/-J3I13L81GDw/TzI5PBONVRI/AAAAAAAAAfA/lcj_rZ2k8e4/s1600/Haarlike-3.pnghttp://2.bp.blogspot.com/-l-KNt0P3waU/TzI5EbCYq9I/AAAAAAAAAe4/vMFZjftJHpY/s1600/Haarlike-2.pnghttp://1.bp.blogspot.com/-VfVkuegsiwU/TzI414grUcI/AAAAAAAAAew/yWbLYpIyYeQ/s1600/image002.png


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Nh vy ta c th thy rng, tnh cc gi tr ca c trng Haar-like, ta

phi tnh tng ca cc vng im nh trn nh. Nhng tnh ton cc gi tr ca

cc c trng Haar-like cho tt c cc v tr trn nh i hi chi ph tnh ton kh

ln, khng p ng c cho cc ng dng thi gian thc. Do Viola v Jones

a ra mt khi nim gi l Integral Image, l mt mng 2 chiu vi kch thc

bng vi kch ca nh cn tnh cc c trng Haar-like, vi mi phn t ca

mng ny c tnh bng cch tnh tng ca im nh pha trn (dng-1) v bn

tri (ct-1) ca n. Bt u t v tr trn, bn tri n vtr di, phi ca nh,

vic tnh ton ny n thun cha trn php cng snguyn n gin, do

tc thc hin rt nhanh.

Hnh 11: Gi tr integral image ti im (x, y) l tng cc im nh phatrn, bn tri.

Cng thc tnh Intergral image

eq. 20

Trong P(x, y): l Intergral image, i(x, y): l nh gc (original image).

Sau khi tnh c Integral Image, vic tnh tng cc gi tr mc xm ca

mt vng bt kno trn nh thc hin rt n gin theo cch sau:

Hnh 12: V d cch tnh nhanh cc gi tr mc xm ca vng D trn nh

Gi s ta cn tnh tng cc gi tr mc xm ca vng D nh trong hnh 11, ta c

thtnh nh sau:

eq. 21


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a qua biu chnh AdaBoost loi bnhanh cc c trng khng c kh

nng l c trng ca i tng. Ch c mt tp nhcc c trng m biu

chnh AdaBoost cho l c khnng l c trng ca khun mt ngi mi c

chuyn sang cho b quyt nh kt qu (l tp cc b phn loi yu). B quyt

nh s xc nhn y li tng cn xc nh nu kt qu ca cc b phn loi

yu xc nhn y li tng cn xc nh.

Mi b phn loi yu s quyt nh kt qu cho mt c trng Haar-like,

c xc nh ngng nh sao cho c thvt c tt c cc b d liu mu

trong tp d liu hun luyn. Trong qu trnh xc nh i tng, mi vng nh

con sc kim tra vi cc c trng trong chui cc c trng Haar-like, nu

c mt c trng Haar-like no xc nhn l i tng cn xc nh th cc c

trng khc khng cn xt na. Th t xt cc c trng trong chui cc c

trng Haar-like sc da vo trng s (weight) ca c trng do AdaBoost

quyt nh da vo s ln v th t xut hin ca cc c trng Haar-like.


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4. OPENCVGSL: h trcho vic xy dng v thit kng dng thc nghim trong kha lun

ny, chng ta s tm hiu v hai th vin OpenCV v GSL - OpenCV h tr

trong vic x l nh, GSL h trcc hm ton hc.

4.1.Th vin OpenCV:OpenCV l vit tt ca Open Source Computer Vision Library. N cha hn 500

hm s dng trong th gic my (computer vision). OpenCV l mt th vin m

ngun m (open source) http://sourceforge.net/. Th vin c vit bng ngn

ng C v C++ c th chy trn cc hiu hnh Linux, Window v Mac OS X.

OpenCV c thit k nng cao hiu sut tnh ton v nhn mnh n h

thng thi gian thc. Mt iu tuyt vi ca OpenCV l n a ra mt h thng

n gin, d s dng gip mi ngi nhanh chng xy dng cc ng dng trong

th gic my, k c cc h thng kim tra trong nh my, bc nh trong lnh vc

y hc, bo mt, r bt hc v..v. N cha cc lp trnh x l nh rt n gin, k

c thc thi cc hm bc cao nh d tm khun mt, theo di khun mt, nhn

dng khun mt.

K tkhi c gii thiu vo thng 1 nm 1999, OpenCV c s dng

trong rt nhiu ng dng, cc sn phm v cc nghin cu. V dtrong lnh vc

hng khng v tr, bn web, s dng gim nhiu trong y hc, phn tch i

tng, an ninh, h thng d tm, theo di tng v h thng bo mt, qun l

h thng sn xut, x l camera, ng dng trong qun s, h thng hng khng

khng ngi li, trn mt t, cc tu ngm.
http://sourceforge.net/http://sourceforge.net/http://sourceforge.net/


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Hnh 15: Qu trnh pht trin ca OpenCV.

Cu trc ca OpenCV c chia thnh cc phn sau:

CV (computer vision): l cung cp cc hm lin quan trc tipn Computer Vision, trong tp trung cc thao tc cp thp

trn nh v camera c th l cc thao tc trong x l nh nh lc

nh, trch bin, phn vng, tm contour, bin i Fourier.

MLL (machine learning library): l th vin machine learning,ci ny bao gm rt nhiu lp thng k v gp cc cng c x

l.

HighGUI: l thnh ph n c h a cc thao tc ln nhng filenh v file video nh c nh, hin thnh, chuyn i nh dng.

CXCore: cha ng rt nhiu cc thnh phn c bn cu thnhnn ton b OpenCV. CxCore bao gm cc cu trc d liu c

bn, cc thao tc ln array, cc cu trc ng, cc hm v, cc

hm tc ng ln d liu, cc hm qun l li v s kin v mt s

hm cn thit khc. Slng hm cha ng trong CxCore l rtln.

IPP (Integrated Performance Primitives): l mt th vin ca Intelgm cc hm ti u mc thp trong cc lnh vc khc nhau, y


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Hnh 17: Minh ha ng dng theo vt i tng.

5.1.2. Xy dng ng dng theo vt i tng da trn m ngun tham kho:Mc tiu l to mt ng dng hu ch v mang tnh thc t da trn m

ngun tham kho:

- p dng thut ton Particle Filter theo vt i tng trn videov camera.

- Theo vt i tng v lu li ng i ca n, lm resource chocc nghin cu khc nh: phn tch hng di chuyn ca cc cu th, theo

di mt ngi trong m ng trong thi gian di

- Nhn dng khun mt v ngi i b, kt hp vi theo vt itng bng camera quan st.

Cch thc hin:

- Xy dng ng dng da trn m ngun thao kho.


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- Thm chc nng theo vt nhiu i tng do ngi dng chnh,da trn chc nng theo di mt i tng ca m ngun tham kho.

- Tham kho cc v d mu ca th vin OpenCV v nhn dng(nhn dng khun mt v ngi i b). Sau , kt hp nhn dng vi

theo di nhiu i tng bng cch thay th vic chn i tng do ngi

dng chnh bng nhn dng.

- S dng ngn ng C++ kt hp lp trnh hng i tng.- Chia tch vic tnh ton v hin th kt qu ln mn hnh thnh cc

tin trnh c lp.

- p dng lp trnh song song tn dng ti b vi x l.5.1.3. Kt hp nhn dng v theo vt i tng:Bn cnh vic ci tin v vic tracking, chng ti cn p dng thm thut

tonnhn dng khun mt v ngi i b. Thay vphi chn i tng xc nh

tracking th nhn dng s h trchng ta trong vic .

Nhn dng khun mt v ngi i b do m ngun OpenCV cung

cp.Vic nhn dng c thc hin da vo file training c sn nh dng xml:

Nhn dng khun mt: haarcascade_frontalface_alt_tree.xml. Nhn dng ngi i b: haarcascade_fullbody.xml.

Qu trnh nhn dng khun mt tng i chnh xc do file training

ng i y , nhng ngc li vic nhn dng ngi i b li cho kt qu

rt thp: ch nhn dng c ngi i bnhn theo phng vung gc vi mt v

lng. iu dn n mt s kt qukhng nh mong i.

5.2.Chng trnh demo:Chng trnh demo tracking gm c 2 phn chnh:

Bng iu khin:oNgn ng: C#.o Chc nng:


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5.2.1. Bng iu khin:Sau y l flow x l ca bng iu khin:


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S lc thng tin cc class nh sau:

Tn class Class cha M t

CameraCapture - Cung cp cc phng

thc ly frame t

camera, tnh ton FPS.

VideoCapture CameraCapture Cung cp cc phng

thc ly frame t

video, ng dn v kch

thc frame ca video.

IRunnable - Interface cung cp cc

phng thc Run v

Stop.

AnyObjectTracking IRunnable Trong phng thc Run,

s tnh ton v tm v tr

ca object (do ngi

dng chn).

DrawingThread IRunnable Hin th hnh nh ln

mn hnhhnh nh

y l kt qusau khi

c tnh ton trong

phng thc Run.

IdentifyObjectTraking AnyObjectTracking Trong phng thc Run,

s tnh ton v tm v tr

ca object (khun mt,

ngi i b).

FrmMain CDialog Mn hnh chnh: hin th

hnh nh, v tr ca

object.


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6. NH GI THC NGHIM:6.1.Cng thc nh gi thc nghim:

Phn ny sa ra c sv s liu v cng thc phn tch v nh gi hiu

qu ca thut ton pht hin v theo vt i tng.

K thut nh gi trong ti liu tham kho [2] xc nh gii hn t ma trn

khong cch gia cc trng tm ca khung xc nh i tng gia ground truth

v kt qu theo vt ca thut ton. Mc gii hn ny c s dng tm s

tng ng gia kt qu theo di ca thut ton v ground truth tnh ton cc i

lng False Positive Track Error, False Negative Track Error, Average

AreaError, v Task Incompleteness Factor. Tuy nhin, cc s liu li ny khng

o lng hiu sut ca thut ton theo di trong trng c s chng cho gia

cc i tng.

Vic thc hin cc s liu nh gi trong ti liu tham kho [1] c chia

thnh cc s liu da trn khung hnh v i tng. i vi s liu da trn

frame true positive, true negative, false positive, and false negative c tnh

ton cho tt c cc khung hnh, v c s dng tnh ton Tracker Detection

Rate, False Alarm Rate, Detection Rate, Speci-ficity, Accuracy, Positive

Prediction, Negative Prediction, False Negative Rate v False Positive Rate. i

vi cc s liu da trn i tng, mi i tng ring bit c s dng tnh

ton true positive, false positive v tng s ground truth tnh ton Tracker

Detection Rate, False Alarm Rate, v Object Tracking Error (OTE). S liu da

trn frame cung cp thng tin v cch thut ton theo di x l cc i tng

trong mt khung hnh, v cc s liu da trn i tng cho php o hiu sut

ca thut ton theo di trn mi i tng c theo di trong thi gian ca

chui video. Trong phm vi ca ti lun vn ny th chng ti ang p dng

cch nh gi ny trong vic nh gi hiu sut ca thut ton theo vt.

Thng tin ca ground truth chnh l khung cha i tng theo vt mi

frame. Tng t, kt qu ca thut ton chnh khung cha i tng theo di

mi frame. Khi thc hin nh gi, c th c nhiu cch kim tra vic chng


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lp gia hai ground truth v kt qu ca thut ton. Trong cch n gin nht

l xem xt nu trng tm ca mt trong hai khung nm trong khung cn li. Cc

i lng sau c s dng trong vic nh gi:

- TN (True Negative): slng frame trn ng dng v ground truthi tng khng xut hin.

- TP (True Positive): slng frame trn ng dng v ground truthi tng xut hin v khung cha i tng trng khp nhau.

- FN (False Negative): s lng frame trn ground truth i tngxut hin, cn ng dng theo vt sai i tng.

- FP (False Positive): s lng frame trn ng dng i tng cxut hin nhng ground truth th khng.

- TRDR (Tracker Detection Rate) v False Alarm Rate: o t l phthin i tng cacc thut tontheo vt, Tracker Detection Rateotc

mmi i tng ring bitc pht hin so vi ground truth.


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Mikel D. Rodriguez, Javed Ahmed, nd Mubarak Shah ActionMACH: A Spatio-temporal Maximum Average Correlation

Height Filter for Action Recognition.

Hnh 19:Hnh nh ca b dliu UCF Sports Action

6.2.3. Highway Traffic Clustering Database:Tp d liu bao gm cc video vxe lu thng trn ng cao tc bao

gm cc nhiu loi xe (xe con, xe ti) vi cc kch thc, mu sc a dng


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BaoCaoKhoaLuan_v1.2_PhuongHTM.docx